To simulate assignment inference for each color scale, we first calculated semantic distance for each pair of endpoint colors (using equation REF  defined in [1]} and reproduced in Supplementary Material Section REF  of this paper). We then determined the optimal assignment (i.e., which assignment had greater merit), and coded the outcome as dark-more = \(+1\)  and light-more = \(-1\) . Last, we multiplied this coding by semantic distance to compute signed semantic distance, which gave positive values to the probability of inferring dark-more assignments and negative values to the probability of inferring light-more assignments.
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